The recommendation task with a textual corpus aims to model customer preferences from both user feedback and item textual descriptions. It is highly desirable to explore a very deep neural network to capture the complicated nonlinear preferences. However, training a deeper recommender is not as effortless as simply adding layers. A deeper recommender suffers from the gradient vanishing/exploding issue and cannot be easily trained by gradient-based methods. Moreover, textual descriptions probably contain noisy word sequences. Directly extracting feature vectors from them can harm the recommender’s performance. To overcome these difficulties, we propose a new recommendation method named the HighwAy recoMmender (HAM). HAM explores a highway mechanism to make gradient-based training methods stable. A multi-head attention mechanism is devised to automatically denoise textual information. Moreover, a block coordinate descent method is devised to train a deep neural recommender. Empirical studies show that the proposed method outperforms state-of-the-art methods significantly in terms of accuracy.

具有文本语料库的推荐任务旨在根据用户反馈和项目文本描述对客户偏好进行建模。非常需要探索一个非常深的神经网络来捕获复杂的非线性偏好。但是，培训更深入的推荐者并不是像简单地添加图层那样轻松。更深的推荐者会遭受梯度消失/爆炸问题的困扰，因此无法通过基于梯度的方法轻松进行训练。此外，文字描述可能包含嘈杂的单词序列。直接从特征向量中提取特征向量可能会损害推荐者的性能。为了克服这些困难，我们提出了一种新的推荐方法，称为HighwAy remMmender（HAM）。HAM探索一种高速公路机制，以使基于梯度的训练方法稳定。设计了一种多头注意力机制来自动对文本信息进行去噪。此外，设计了块坐标下降法来训练深度神经推荐器。实证研究表明，在准确性方面，所提出的方法明显优于最新方法。